1. Field of the Invention
The present invention relates to techniques for automatically detecting problems in systems. More specifically, the present invention relates to a method and an apparatus that facilitates automatic high-sensitivity detection of anomalies in a signal.
2. Related Art
Modern server computer systems are typically equipped with a significant number of sensors which monitor signals during the operation of the computer systems. Results from this monitoring process can be used to generate time series data for these signals which can subsequently be analyzed to determine how a computer system is operating. One particularly desirable application of this time series data is for purposes of “proactive fault monitoring” to identify leading indicators of component or system failures before the failures actually occur.
Unfortunately, many of these computer systems use low-resolution eight-bit analog-to-digital (A/D) converters in their physical sensors to sample the signals. This causes readings of physical variables such as voltage, current, and temperature to be highly quantized. Hence, the sampled signal values from these sensors can only assume discrete values, and no readings can be reported between these discrete values. For example, voltages for system board components may be quantized to the nearest 10 mV; e.g. 1.60 V, 1.61 V, 1.62 V, etc. Hence, if the true voltage value is 1.6035 V, it can only be reported as one of the quantized values, 1.60 or 1.61.
Note that the above-described quantization effect presents a serious problem for proactive fault monitoring. Normally, one can apply statistical pattern recognition techniques to continuous signal values to detect if the signals start to drift away from steady-state values at a very early stage of system degradation. However, with significant quantization, conventional statistical pattern recognition techniques cannot be used effectively to detect the onset of subtle anomalies that might precede component or system failures.
To overcome this quantization problem, researchers have used a moving histogram technique to represent each quantized physical signal and also use multi-hypothesis Sequential Probability Ratio Tests (SPRTs) to detect subtle changes in the signal. The moving histogram technique when combined with the SPRT technique has demonstrated promising sensitivity and robustness, even when the variations in the physical variables are a small percentage of the quantization resolution.
Unfortunately, the conventional moving histogram techniques suffer from a serious drawback over time. Specifically, when a system is monitored for a long period of time and exhibits no signal degradation, the conventional moving histogram technique builds up “inertia” in the collected data which makes it less sensitive in detecting the onset of subtle degradation. In other words, the sensitivity, as well as the robustness of the fault detection, decreases as the monitoring time increases.
What is needed is a method and an apparatus for facilitating high-sensitivity detection of anomalies in a signal without the above-described problems.